NutriPlot

Why this graph ?

Consuming foods rich in protein but low in calories plays a crucial role in managing body weight and reducing body fat percentage. Moreover, high-protein foods often induce a stronger feeling of satiety. When tailoring a diet to enhance muscle growth, comprehending the calorie and protein composition of various foods is essential. The objective of this graph is to determine which foods offer the highest protein content per fixed amount of calories.

Hence, I designed this scatter plot to facilitate visualizing which foods offer ample protein while being economical in calories.

However, there are limitations to consider when interpreting this graph:

• Firstly, the Y-axis may seem somewhat counter intuitive as it represents protein density per 100 kcal.
  
• The satiety levels associated with each food vary significantly and could significantly influence dietary choices.
  
• It’s important to note that prioritizing high-protein foods doesn’t imply exclusive reliance on them; a balanced diet, devoid of excess, remains the optimal recommendation for most individuals.

How to read this scatter plot

On the axis, as you move towards the right, the food becomes more calorie-dense. Similarly, moving upwards indicates increasing protein density.

Consequently:

• Top left indicates foods with high protein content per calorie and low calorie count per 100g.
  
• Bottom right represents foods with low protein content per calorie but high overall calorie count per 100g.
  
• Top right denotes foods with high protein content per calorie and high calorie count per 100g.
  
• Bottom left signifies foods with low protein content per calorie and low calorie count per 100g.

source("libraries.R")

dr <- read_csv("prot.csv", show_col_types = FALSE )

df <- dr %>% 
  mutate(perc_prot = prot_per_100g/kcal_per_100g*100, prot_per_serving = prot_per_100g *serving /100,
         kal_per_serving = kcal_per_100g*serving /100)

scatter_plot1 <- df %>% 
  ggplot() +
  # Add points to the plot with kcal on x-axis and perc_prot on y-axis
  geom_point(aes(x = kcal_per_100g, y = perc_prot, color = perc_prot, text = name)) +    
  # Add text labels using geom_text_repel
  geom_text_repel(aes(x = kcal_per_100g, y = perc_prot, label = name), force = 6) +  
  scale_x_continuous(n.breaks = 10,limits = c(0,700)) +
  scale_y_continuous(n.breaks = 10,limits = c(0,23)) +
  scale_color_gradient(low = 'red',high = "green") +
  theme(legend.position = "none") +
  labs(y = "Protein Density [%]", x = "kcal for 100 grams of food ")    

scatter_plot1

Figure 1: for 100g of content

table_recap <- df %>% 
  select(name,kcal_per_100g,prot_per_100g,perc_prot) %>% 
  dplyr::arrange(desc(perc_prot)) %>% 
  kable(col.names = c("names","Kcal for 100g","grams protein for 100g of food","Protein Density [%]"),
        digits = 2)


table_recap

names	Kcal for 100g	grams protein for 100g of food	Protein Density [%]
Tuna	115	27.00	23.48
Hake	64	15.00	23.44
Whey	377	83.00	22.02
Cod	81	17.00	20.99
Hake Fillet	81	17.00	20.99
Skyr	61	12.00	19.67
Turkey	119	21.70	18.24
Skimmed Quark	63	10.00	15.87
Chicken Breast	195	29.50	15.13
Lean Ground Beef	137	20.00	14.60
Cottage Cheese	97	12.00	12.37
Light Mozzarella	165	20.00	12.12
Tofu	126	13.00	10.32
Skimmed Milk	33	3.30	10.00
Protein Bar	355	35.00	9.86
Salmon	202	19.00	9.41
Light Feta	174	16.00	9.20
Parmesan	392	35.00	8.93
Greek Yogurt	114	10.00	8.77
Eggs	147	12.50	8.50
Zucchini	16	1.21	7.56
Chickpeas	128	9.00	7.03
Mixed Minced Meat	256	18.00	7.03
Gruyère	413	29.00	7.02
Fish finger	190	13.00	6.84
Muesli	410	28.00	6.83
Green Peas	77	5.20	6.75
Red Beans	132	8.80	6.67
Black Beans	91	6.00	6.59
Bacon Bits	270	17.00	6.30
Lentils	165	8.30	5.03
Peanuts	567	25.80	4.55
Red Bell Pepper	26	1.00	3.85
Oats	357	13.50	3.78
Almonds	578	21.26	3.68
Pasta	371	13.00	3.50
Quinoa	143	5.00	3.50
Almond Milk	15	0.50	3.33
Chia Seeds	490	15.00	3.06
Walnuts	654	15.00	2.29
Banana	89	2.00	2.25
Steamed Potatoes	85	1.70	2.00
Apples	52	0.26	0.50

Caption